A new approach for clustering alarm sequences in mobile operators

Abstract

Telecom Networks produce huge amount of daily alarm logs. These alarms usually arrive from different regions and network equipments of mobile operators at different times. In a typical network operator, Network Operations Centers (NOCs) constantly monitor those alarms in a central location and try to fix issues raised by intelligent warning systems by performing a trouble ticketing based management system. In order to automate rule findings, different sequential rule mining algorithms can be exploited. However, the number of sequential rules and alarm correlations that can be generated by using these algorithms can overwhelm the NOC administrators since some of those rules are neither utilized nor reduced appropriately by the non-customized sequential rule mining algorithms. Therefore, additional efficient and intelligent rule identification techniques need to be developed depending on the characteristic of the data. In this paper, two new metrics that is inspired from document classification approaches are proposed in order to increase the accuracy of the sequential alarm rules. This approach utilizes new definition of identifying transactions as alarm features and clustering the alarms by their occurrences in built transactions. Experimental evaluations demonstrate that up to 61% accuracy improvements can be achieved through utilizing the proposed appropriate metrics compared to a sequential rule mining algorithm.